System for obtaining and classifying energy characteristics

ABSTRACT

An approach and system for receiving data from a thermostat in a building, treating the data as representative of a thermal response model or system, and determining a relationship between a rate of building internal temperature change and change in outdoor temperature to provide an indication of how well a building is insulated versus a thermal mass of the building.

This application claims the benefit of U.S. Provisional Application Ser.No. 61/807,184, filed Apr. 1, 2013, and entitled “Classification ofBuilding Energy Characteristics Based on Connected Thermostat Data”.U.S. Provisional Application Ser. No. 61/807,184, filed Apr. 1, 2013, ishereby incorporated by reference.

BACKGROUND

The present disclosure pertains to energy usage and particularly to timeconstants in buildings.

SUMMARY

The disclosure reveals an approach and system for receiving data from athermostat in a building, treating the data as representative of alumped capacity model or system, and determining a relationship betweena rate of building internal temperature change and change in outdoortemperature to provide an indication of how well a building is insulatedversus a thermal mass of the building, which may lead to a thermal timeconstant of the building.

BRIEF DESCRIPTION OF THE DRAWING

FIG. 1 is a diagram of an illustrative example setup for effecting thepresent system and process;

FIG. 2 is a diagram of a graph revealing setpoints and temperatures fora building in Omaha, Nebr., shown for a month of January;

FIG. 3 is a diagram of a graph revealing the same information as thegraph of FIG. 2 but with setpoint change data for a period betweenmidnight and 2:30 AM of a day in January;

FIG. 4 is a diagram of a graph for a period between 9:00 PM and 2:00 AMof another day in January, with setpoint change data and revealing thesame information as that in the graph of FIG. 2;

FIG. 5 is a diagram of a graph revealing setpoint change data with sameinformation as that in the graph of FIG. 2 but for a period between 2:00PM and 9:30 PM of another day in January;

FIGS. 6, 7, 8, 9, 10 and 11 are diagrams of graphs revealing buildingsetpoints and temperatures for buildings generally during a month ofJanuary at various locations and the graphs of FIGS. 2-11 may beregarded as exploratory;

FIG. 12 is a diagram of equations that may be used in a thermal responseanalysis for a heating season and an example or instance of thatanalysis may assume a lumped capacity model or system;

FIG. 13a is a graph of data plotted as |dT/dt| versus delta T frombuilding data;

FIG. 13b is a graph of the data plotted as |dT/dt| versus delta T wherepatterns may emerge for different time constants;

FIG. 13c is a diagram of setpoint temperature, temperature datapointsand a line from the data;

FIG. 13d and FIG. 13e are diagrams of graphs, respectively, that reveala comparison after separating cooling and heating;

FIG. 14 is a diagram of a plot of |dT/dt| versus delta T for variousconditions relating to a temperature, heat setpoint, cool setpoint andoutdoor temperature;

FIG. 15 is a diagram of a graph of mean |dT/dt| versus bin avg |Toa-T|of data in a plot;

FIG. 16 is a diagram of a bar graph indicating time constants of variousfacilities at different geographical locations;

FIG. 17 is a diagram of a bar graph similar to the graph of FIG. 16 witha time constant for more instances of facilities or buildings at variouslocations;

FIG. 18 is a diagram of bar graph similar to the graphs of FIGS. 16 and17 with time constants for additional instances of facilities orbuildings at various locations and the graphs may be regarded asexploratory; and

FIG. 19 is a diagram of a computer system for implementing one or moreapproaches and algorithms according to illustrative examples.

DESCRIPTION

The present system and approach may incorporate one or more processors,computers, controllers, user interfaces, wireless and/or wireconnections, and/or the like, in an implementation described and/orshown herein.

This description may provide one or more illustrative and specificexamples or ways of implementing the present system and approach. Theremay be numerous other examples or ways of implementing the system andapproach.

Algorithms or functions may be implemented in software or a combinationof software and human implemented procedures in an example. The softwaremay consist of computer executable instructions stored on computerreadable media such as memory or other type of storage devices. Further,such functions may correspond to modules, which are software, hardware,firmware or any combination thereof. Multiple functions may be performedin one or more modules as desired. The software may be executed on adigital signal processor, ASIC, microprocessor, or other type ofprocessor operating on a computer system, such as a personal computer,server or other computer system.

Networked thermostats or connected thermostats may be capable ofproviding data regarding temperatures and setpoints corresponding to thebuildings whose temperatures they regulate. Together with otherinformation, such data may be used to create models of buildingsregarding energy utilization.

Connected thermostats may generate large amounts of data. Analytics onthis data may provide very useful insights as a forecast of demandresponse potential, operational setpoint recommendations for savings,energy efficiency upgrade recommendations, diagnosis of equipmentissues, and so forth.

Connected thermostats may produce a limited amount of time-series datasuch as indoor temperature ( ) and humidity, temperature setpoints,outdoor temperature and humidity, and heating or cooling mode. The datamay be recorded asynchronously, such as when change occurs). In order toextract values from the time series data, a simple lumped capacity modelof the building or house may be created. Some parameters related tothermal capacity and energy efficiency of the building may be extractedfor classifying the buildings as groups with respect to the parametersand external weather conditions.

By likening the building to, for example, a lumped capacity model orsystem (functioning as one unit for thermal modeling consideration),various parameters, such as specific heat capacity, mass, heat transfercoefficient, surface area and internal heating/cooling sourceinformation may be assigned to a building. An equation may represent therate of change of indoor temperature with time, given an outdoortemperature and the noted parameters.

FIG. 12 is a diagram of equations that may be used in a thermal responseanalysis for a heating season. An example or instance of that analysismay assuming a lumped capacity model or system. Symbol 93 representsthermal mass. Symbol 94 represents heat capacity. Symbols 93 and 94together represent thermal or heat capacity (also called thermal mass).

Symbol 95 represents the rate of heat or energy provided by a furnaceinto the building. Symbol 96 represents an amount of heat or energy thatescapes form the building. Symbol 97 represents an overall heat transfercoefficient for the building. Symbol 98 represents the total surfacearea of the building. Symbols 97 and 98 represent the total heattransfer or loss from the building per degree of temperature. Symbol 99represents the indoor temperature of the building. Symbol 101 representsthe outdoor temperature of the building. Equation 102 represents a timeconstant of the building. Thus, when furnace heating or air conditionercooling requests are zero (e.g., night setback), the relationshipbetween the rate of room temperature change and delta T (T-Toa) mayprovide an indication of how well the building is insulated versus thethermal mass of the building.

Categories of building characteristics may include: poorly insulated andsmall thermal mass for a small time constant; well insulated and smallthermal mass for a moderate time constant; poorly insulated and a largethermal mass for a moderate time constant; and well insulated and alarge thermal mass for a large time constant.

Other classifications of building energy characteristics may incorporatea rate of temperature change at a particular weather condition forparticular type of buildings at particular setbacks. By computing a rateof room temperature change and correlating it with a difference betweenoutdoor and indoor temperatures, a time constant parameter may beestimated. Different buildings may be compared among one another andwith themselves at different points in time.

Buildings may be classified according to their temperature rate ofchange during different outdoor weather conditions, which may work wellfor large data applications when small amounts of data from a large setof devices need to be processed.

Additional data (such as thermostat relay position, house orheating/cooling source specifications) and/or more frequent data may beused to create a building model for predicting energy usage.

An analytic capability may be built into a system that collects andprocesses thermostat data. As thermostat data from different regionsbecome available, a computer may run the data through analytic software.

Results of an approach may incorporate a current estimate of buildingtime constants, plots indicating comparisons among various buildings andchanges from the previous updates, and other parameters that are derivedfrom the process. The process may lead to automated anomaly detection,recommendations for operator action, or owner action for energy savingmeasures.

FIG. 1 is a diagram of an illustrative example setup for effecting thepresent system and process. A computer 800 may have a connection withthermostats (1) 91 through (M) 92 of buildings (1) 81 through (N) 82. Mand N may represent a small number or large numbers. The buildings maybe of different types (including residences) and be in variousgeographical locations. The connections of the thermostats to computer800 may be direct or indirect, wireless or wire, or effected in otherways. For instance, computer 800 may be connected to the thermostats viaa communications network 88 or cloud. Computer 800 may a mechanism foroperating the present system. Computer 800 may have other connectionswith the buildings besides the thermostats. Computer 800 may connectwith and utilize applications 83, storage and databases 84, processing85, analytics 86, servers 87, and so forth, via the communicationsnetwork 88, such as for example, the Internet. Computer 800 is furtherdiscussed relative to FIG. 19.

FIG. 13a is a graph 67 of data plotted as |dT/dt| versus |Toa−T| (deltaT) from building data. Each point may be calculated from a dT/dt fromthe formulas of FIG. 12. FIG. 13b is a graph 68 of the data plotted as|dT/dt| versus |Toa−T| where patterns 71, 72 and 73 of a small timeconstant, moderate time constant and large time constant, respectively,in the cooling situation may emerge for particular locations ordifferent types of buildings. Patterns are also shown for the heatingsituation in the lower left of the graph. The patterns may be regardedas time constants, but not necessarily in terms of classical thermaltime constants.

Patterns 71, 72 and 73 may be approximations since several parametersare not necessarily taken into account, such as for example solar gain,occupancy, adjacency to interior spaces, and so on. However, theapproximations may be efficient and reliable for purposes of determiningbuilding energy characteristics. Additional information such as weather(e.g., solar gain) may be overlaid to better reveal a pattern forparticular locations or types of buildings in terms of |dT/dt| versus|Toa−T| in graphs 67 and 68.

A procedure for determining building energy characteristics particularlywith connected thermostats may be noted. Data may be collected forbuildings, setpoint changes may be detected for cooling or heating, suchas a setpoint change being a night setback (e.g., a heating mode with asetpoint decrease or a cooling mode with a setpoint increase). One maynote if a setpoint change is accompanied by a rather long period of aconstant setpoint before and after the change. A variance may be noted.One may get a slope of disp T change upon a duration (e.g., one-halfhour) after a setpoint change. There may be a linear fit for a slopewith enough data, or a simple slope calculation may be made. An outdoortemperature change may be obtained during the same period. A save may bemade of dT/dt and (Toa−Tdisp) along with parameters, device ID, locationand information at hand. The parameters may be analyzed. Data cleaningmay be used to remove anomalous measurements. Other corrections mayapply.

Heating and cooling data may be separated out since behavior of theoccupants or the building may be different during two seasons. There maybe further separation for different outside air temperature ranges bybinning the slope dT/dt within bins of delta T (Toa−Tin).

There may be an improved line fit for a more accurate model. Linefitting through robust regression may be used for taking care ofoutliers, i.e., iterative weighting of data points. Line fitting throughtotal least squares and principal components fit may be used to takecare of asymmetry in a line fit. There may be statisticalcharacterization of the time constant to characterize the variability inthe time constant and to provide uncertainty quantification toapplications that use this analysis. Variability may be quantified withconfidence intervals. These items of further analysis may bring moreaccurate conclusions.

FIG. 13c is a diagram of setpoint temperature 103, temperaturedatapoints 104, and a line 105 fit to get dT/dt from datapoints 104 atsetpoint temperature 103 change. Line 106 may be an ideal model curve.

FIG. 13d and FIG. 13e are diagrams of graph 108 and graph 109,respectively, that reveal a comparison after separating cooling andheating. The graphs show delta temperature (Tout-Tin) versus slope(delta temp/delta time). Cooling data is shown approximately by anencircling line 111 and heating data is shown approximately by anencircling line 112. A time constant 113 may be revealed for the coolingsituation in graph 108. A time constant 114 may be revealed for theheating situation in graph 109.

Demand response (DR) customization may be noted. Building performancemay be predicted for given weather conditions with confidence bounds.One may customize a thermostat DR signal to known buildingcharacteristics. For a building with quick internal heat gain, athermostat turndown level may need to be higher to get a guaranteedresponse; or one may keep thermostat turndown level lower to avoidcomfort issues. For DR recruitment, one may select buildings based onperformance characteristics

As to energy efficiency, one may compare building to its pastperformance by noting whether the building is in its range for thermalloss. One may compare the building to others in the region to improveefficiency. As to building energy maintenance, the building performancetrend may be watched by asking whether the building thermal loss is inthe range of past behavior.

Significance relative to a virtual power plant may be noted. There maybe a capability to control an aggregated group of loads virtual powerplants. A use of thermostatically controlled loads (TCLs) may beconsidered for this purpose since they have built-in thermalcapacitance. The virtual power plants may be an alternative to buildingnew power plants, or keeping generators on to manage renewablevariability. However, virtual power plants do not necessarily have thesame controllability as traditional generators. There may be a need ofaccurate models of the behavior of aggregated loads and their responseto DR signals. The thermostat data analysis noted herein may have adirect application into aggregating HVAC loads to develop a virtualpower plant application.

FIGS. 2-11 and 14-18 are diagrams of graphs and bar charts that mayillustrate various setpoints and temperatures for different devices atvarious locations, according to various examples, and can be regarded asexploratory and for illustrative purposes.

FIG. 2 is a diagram of a graph 21 revealing setpoints and temperaturesfor a building in Omaha, Nebr. Disp (indoor) temperature 31, heatsetpoint 32, cool setpoint 33 and outdoor temperature 34 are shown for amonth of January. FIGS. 2-11 are diagrams with thermostatic data and maybe for illustrative purposes.

FIG. 3 is a diagram of a graph 22 revealing the same information asgraph 21 but with setpoint change data for a period between midnight and2:30 AM of a day in January.

FIG. 4 is a diagram of a graph 23 for a period between 9:00 PM and 2:00AM of another day in January, with setpoint change data and revealingthe same information as that in graph 21 of FIG. 2.

FIG. 5 is a diagram of a graph 24 revealing setpoint change data withsame information as that in graph 21 of FIG. 2 but for a period between2:00 PM and 9:30 PM of another day in January.

FIGS. 6, 7, 8, 9, 10 and 11 are diagrams of graphs 25, 26, 27, 28, 29and 30, respectively, revealing building setpoints and temperatures forbuildings generally during a month of January at various locations. Thegraphs reveal disp temperature 31, heat setpoint 32, cool setpoint 33and outdoor temperature 34. Graphs 25-30 reveal information similar tothat of graph 21 for Omaha, Nebr. Graphs 25, 26, 27, 28, 29 and 30 haveinformation from locations at St. Petersburg, Fla., North Canton, Ohio,Kings Mountain, N.C., Brooklyn Park, Minn., Warsaw, Ind., and Lomira,Wis., respectively.

FIG. 14 is a diagram of a plot 65 of |dT/dt| versus |Toa−T| for variousconditions relating to disp temperature 41, heat setpoint 42, coolsetpoint 43 and outdoor temperature 44. Plot 65 may be regarded asschematic rather than actual. FIG. 15 is a diagram of a graph 66 of mean|dT/dt| versus bin avg |Toa−T|of plot 65. Graph lines 51, 52, 53 and 54relate to data points 41, 42, 43 and 44, respectively.

FIG. 16 is a diagram of a bar graph 61 indicating time constants ofvarious facilities at different geographical locations. A scale of timeconstant “m·Cp/UA” may range from about −600 to about +1700 units.Locations may be from around the country. However, a location may berepeated, for instance, Lomira, Wis. A time constant may be differentfor each instance of a location since a facility or building for eachtime constant can be different.

FIG. 17 is a diagram of a bar graph 62 similar to graph 61 with a timeconstant for more instances of facilities or buildings at variouslocations. Some locations may be the same as those in graph 61.Likewise, FIG. 18 is a diagram of bar graph 63 similar to graphs 61 and62 with time constants for additional instances of facilities orbuildings at various locations. Some of the locations may be similar tothose in graphs 61 and 62. These graphs may be regarded as exploratory.

FIG. 19 is a diagram of a computer system 800 for implementing one ormore approaches and algorithms according to illustrative examples. Inone example, multiple computer systems may be utilized in a distributednetwork to implement multiple components in a transaction basedenvironment. An object-oriented, service-oriented, or other architecturemay be used to implement such functions and provide communications amongmultiple systems and components. One example computing device in theform of a computer 800 may include a processing unit 802, memory 803,removable storage 810, and non-removable storage 812.

Memory 803 may have a volatile memory 814 and a non-volatile memory 808.Computer 800 may incorporate or have access to a computing environmentthat has a variety of computer-readable media, such as volatile memory814 and non-volatile memory 808, removable storage 810 and non-removablestorage 812. Computer storage may incorporate random access memory(RAM), read only memory (ROM), erasable programmable read-only memory(EPROM), and electrically erasable programmable read-only memory(EEPROM), flash memory and/or other kinds of memories. Storagemechanisms may also incorporate compact disc read-only memory (CD ROM),digital versatile disks (DVD), optical disks, magnetic cassettes,magnetic tape, magnetic disks or other magnetic storage devices, or anyother medium capable of storing computer-readable instructions,information and data.

Computer 800 may have access to a computing environment that includesinput 806, output 804, and a communication connection 816. The computermay operate in a networked environment using a communication connectionto connect to one or more remote computers, such as database servers.

Computer 800 may facilitate use cloud processing, data accession,obtaining data and sensing from various locations, data storage, and thelike, for effecting the present system and approach for classifyingbuilding energy characteristics based on connected thermostat data.

A remote computer may incorporate a personal computer (PC), server,router, network PC, a peer device or other common network node, or thelike. The communication connection may include a local area network(LAN), a wide area network (WAN) and/or other networks.

Computer-readable instructions stored on a computer-readable medium maybe executable by the processing unit 802 of the computer 800. A harddrive, CD-ROM, and RAM may be examples of articles including anon-transitory computer-readable medium. For example, a computer program818 may be capable of providing a generic technique to perform accesscontrol, check for data access and/or doing an operation on one of theservers in a component object model (COM) based system which may beincorporated on a CD-ROM and loaded from the CD-ROM to a hard drive.Computer-readable instructions may allow computer 800 to provide genericaccess controls in a COM based computer network system having multipleusers and servers.

To recap, a system for classifying characteristics of buildings mayincorporate a computer, and one or more thermostats situated in one ormore buildings, respectively, and have a connection to the computer.

Each of the one or more thermostats may provide time series data to thecomputer. The time series data may incorporate indoor temperature,outdoor temperature, and temperature setpoints relative to time. Athermal response model for each of the one or more buildings may bederived from the time series data. A connection may have one or moreintermediary connections. A connection may be wire, wireless, or wireand wireless.

The model may approximate thermal mass with respect to the heat losscharacteristics for a building. A relationship between the thermal massand the heat loss characteristics may define a time constant for abuilding.

A time constant of a building may be represented by(mc_(p)/UA)=((T_(oa)−T_(in))/(dT/dt)). mc_(p) may represent thermal massof the building. UA may represent heat transfer or loss from thebuilding per degree of temperature. T_(oa) may represent temperatureoutside of the building. T_(in) may represent temperature inside of thebuilding.

A time constant of a building may be estimated from correlating a rateof temperature change in the building with a difference between theoutdoor temperature and the indoor temperature of the building.

The system may further incorporate classifying buildings according totime constants of the respective buildings.

The time constant of a building may be refined with additional data. Theadditional data may incorporate one or more items from a groupconsisting of heating source specifications, cooling sourcespecifications, building insulation information, building size, andsurface areas relevant to the building. Insulation information mayinclude a nature and properties of a building structure, R value ofinsulation, environmental and ambient conditions about the building, andso on.

The one or more thermostats may be connected to the computer via acommunications network.

The communications network may incorporate access to one or more itemsselected from a group consisting of processing, memory, applicationservers, sensors, databases, specifications for the one or morebuildings, specifications of thermostats, receipt and storage oftime-series data from the one or more thermostats, and derivation of thethermal response model for each of the one or more buildings from thetime-series data.

The one or more thermostats may be connectable or connected to anetwork. The one or more thermostats may be viewable and operable by asmart device via the network. The one or more thermostats may beconnectable to the network via a wire or wireless medium or media suchas WiFi, or the one or more thermostats may be controllable by the smartdevice via a wire or wireless medium or media such as WiFi.

An approach for classifying energy characteristics of buildings mayincorporate obtaining data from one or more thermostats in one or morebuildings, creating a thermal response model of a building from thedata, extracting parameters from the thermal response model for derivingthermal capacity and energy efficiency of the building, and classifyingthe one or more buildings as groups with respect to the parameters andweather conditions external to the one or more buildings.

The one or more thermostats may be connectable or connected to acommunications network.

The approach may further incorporate providing a computer connectable orconnected to the communications network. The computer may provideprocessing to obtain the data from the one or more thermostats, createthe thermal response model of a building from the data, extractparameters from the thermal response model, or classify the one or morebuildings with respect to the parameters.

The approach may further incorporate providing a computer connectable orconnected to the communications network. The computer may be operated tohave the data from the one or more thermostats to be put in a storageconnected to the communications network, to create a thermal responsemodel of a building by a processor connected to the communicationsnetwork, to extract parameters from the thermal response model by aprocessor connected to the communications network, or to classify theone or more buildings with respect to the parameters by a processorconnected to the communications network.

The approach may further incorporate deriving a time constant for eachof the one or more buildings from a relationship of insulationinformation and thermal mass of each of the one or more buildings,respectively.

A mechanism for determining energy characteristics may incorporate oneor more thermostats situated inside one or more buildings, respectively,and a computer connected to the one or more thermostats. The one or morethermostats may provide time-series data to the computer. Thetime-series data may incorporate one or more items selected from a groupconsisting of indoor temperatures, indoor humidity, temperaturesetpoints, outdoor temperatures, outdoor humidity, heating mode, andcooling mode.

The time series data may be recorded asynchronously.

A thermal response model of each of the one or more buildings may bedeveloped from the time series data. One or more parameters related tothermal capacity and energy efficiency, determined together for each ofthe one or more buildings, may be extracted from the respective thermalresponse model.

The one or more parameters may be selected from a group consisting ofbuilding insulation information, building size, surface areas,information about a heating source and information about a coolingsource.

A rate of indoor temperature change inside a building and a differencebetween indoor temperature and outdoor temperature of each of the one ormore buildings may indicate insulation information respectively for eachof the one or more buildings. A thermal mass may be obtained for each ofthe one or more buildings. A time constant may be determined for each ofthe one or more buildings from an insulation information and thermalmass of each of the one or more buildings.

The time constant may be used for one or more items of a groupconsisting of classifying buildings, identifying patterns of buildingsresembling particular geographical locations, identifying patterns ofbuildings indicating particular types of buildings, detecting patternsresembling anomalous energy performance of a building, and identifyingpatterns that indicate degradation of energy performance of a buildingover time.

In the present specification, some of the matter may be of ahypothetical or prophetic nature although stated in another manner ortense.

Although the present system and/or approach has been described withrespect to at least one illustrative example, many variations andmodifications will become apparent to those skilled in the art uponreading the specification. It is therefore the intention that theappended claims be interpreted as broadly as possible in view of therelated art to include all such variations and modifications.

What is claimed is:
 1. A demand response system for classifying characteristics of buildings and controlling thermostats, the system comprising: a computer; and one or more thermostats situated in one or more buildings, respectively, and having a connection to the computer; and wherein: each of the one or more thermostats provides time series data to the computer; the time series data comprise indoor temperature, outdoor temperature, and temperature setpoints relative to time; a thermal response model for each of the one or more buildings is derived from the time series data; a connection includes one or more intermediary connections; and the one or more intermediary connections are wired, wireless, or wired and wireless, a relationship between thermal mass and heat loss characteristics of a building defines a time constant for a building and the time constant of a building is represented by (mc_(p)/UA)=((T_(oa)−T_(in))/(dT/dt)), wherein: mc_(p) represents thermal mass of the building; UA represents heat transfer or loss from the building per degree of temperature; T_(oa) represents temperature outside of the building; T_(in) represents temperature inside of the building; and (dT/dt) represents a rate of change of temperature inside of the building; and a demand response control signal is provided from the computer to at least one thermostat of the one or more thermostats to adjust the operation of the at least one thermostat, the demand response control signal is customized to adjust operation of the at least one thermostat based on the relationship between the thermal mass and heat loss characteristic of a building of the one or more buildings in which the at least one thermostat is situated.
 2. The system of claim 1, wherein the thermal response model approximates thermal mass with respect to the heat loss characteristics for a building.
 3. The system of claim 1, wherein the time constant of a building is estimated from correlating a rate of temperature change in the building with a difference between the outdoor temperature and the indoor temperature of the building.
 4. The system of claim 3, further comprising classifying buildings according to time constants of the respective buildings.
 5. The system of claim 3, wherein: the time constant of a building is refined with additional data; and the additional data comprises one or more items from a group consisting of heating source specifications, cooling source specifications, building insulation information, building size, and surface areas relevant to the building.
 6. The system of claim 1, wherein the one or more thermostats are connected to the computer via a communications network.
 7. The system of claim 6, wherein the communications network comprises access to one or more items selected from a group consisting of processing, memory, application servers, sensors, databases, specifications for the one or more buildings, specifications of thermostats, receipt and storage of time-series data from the one or more thermostats, and derivation of the thermal response model for each of the one or more buildings from the time-series data.
 8. The system of claim 1, wherein: the one or more thermostats are connectable or connected to a network; the one or more thermostats are viewable and operable by a smart device via the network; and the one or more thermostats are connectable to the network via a wire or wireless medium or media, or the one or more thermostats are controllable by the smart device via a wire or wireless medium or media.
 9. A demand response method for classifying buildings and controlling thermostats, the method comprising: obtaining time series data from one or more thermostats in one or more buildings; creating a thermal response model of a building from the time series data; extracting parameters from the thermal response model; classifying the one or more buildings as groups with respect to the parameters and weather conditions external to the one or more buildings, deriving a time constant for each of the one or more buildings from a relationship of insulation information and thermal mass of each of the one or more buildings, respectively; sending a demand response control signal to at least one thermostat of the one or more thermostats to adjust operation of the at least one thermostat, the demand response control signal is customized to adjust operation of the at least one thermostat based on the derived time constant of a building of the one or more buildings in which the at least one thermostat is situated; and wherein: the parameters include one or more of specific heat capacity, mass heat transfer coefficient, and internal heating/cooling source information; the time series data includes one or more of indoor temperature relative to time, outdoor temperature relative to time, and temperature setpoints relative to time; and the time constant of a building is represented by (mc _(p) /UA)=((T _(oa) −T _(in))/(dT/dt)), wherein: mc_(p) represents thermal mass of the building; UA represents heat transfer or loss from the building per degree of temperature; T_(oa) represents temperature outside of the building; T_(in) represents temperature inside of the building; and (dT/dt) represents a rate of change of temperature inside of the building.
 10. The method of claim 9, wherein the one or more thermostats are connectable or connected to a communications network.
 11. The method of claim 10, further comprising: providing a computer connectable or connected to the communications network; and wherein the computer provides processing to obtain the time series data from the one or more thermostats, create the thermal response model of a building from the time series data, extract parameters from the thermal response model, or classify the one or more buildings with respect to the parameters.
 12. The method of claim 10, further: comprising providing a computer connectable or connected to the communications network; and wherein the computer is operated to have the time series data from the one or more thermostats to be put in a storage connected to the communications network, to create a thermal response model of a building by a processor connected to the communications network, to extract parameters from the thermal response model by a processor connected to the communications network, or to classify the one or more buildings with respect to the parameters by a processor connected to the communications network.
 13. A demand response mechanism for determining energy characteristics and controlling thermostats, the mechanism comprising: one or more thermostats situated inside one or more buildings, respectively; and a computer connected to the one or more thermostats; and wherein: the one or more thermostats provide time-series data to the computer; the time-series data comprise one or more items selected from a group consisting of indoor temperatures, indoor humidity, temperature setpoints, outdoor temperatures, outdoor humidity, heating mode, and cooling mode; a time constant is determined for at least one of the one or more buildings, the time constant of a building is represented by (mc_(p)/UA)=((T_(oa)−T_(in))/(dT/dt)), wherein: mc_(p) represents thermal mass of the building; UA represents heat transfer or loss from the building per degree of temperature; T_(oa) represents temperature outside of the building; T_(in) represents temperature inside of the building; and (dT/dt) represents a rate of change of temperature inside of the building; and a demand response control signal is provided from the computer to at least one thermostat of the one or more thermostats to adjust operation of the at least one thermostat, the demand response control signal is customized to adjust operation of the at least one thermostat based on the time constant of a building of the one or more buildings in which the at least one thermostat is situated.
 14. The mechanism of claim 13, wherein the time series data is recorded asynchronously.
 15. The mechanism of claim 13, wherein: a thermal response model of each of the one or more buildings is developed from the time series data; and one or more parameters related to thermal capacity and energy efficiency, determined together for each of the one or more buildings, are extracted from the respective thermal response model.
 16. The mechanism of claim 15, wherein the one or more parameters are selected from a group consisting of building insulation information, building size, surface areas, information about a heating source and information about a cooling source.
 17. The mechanism of claim 13, wherein: the rate of temperature change inside a building and a difference between indoor temperature and outdoor temperature of each of the one or more buildings indicate insulation information respectively for each of the one or more buildings; a thermal mass is obtained for each of the one or more buildings; and the time constant is determined for each of the one or more buildings from the insulation information and the thermal mass of each of the one or more buildings.
 18. The mechanism of claim 13, wherein the time constant is used for one or more items of a group consisting of classifying buildings, identifying patterns of buildings resembling particular geographical locations, identifying patterns of buildings indicating particular types of buildings, detecting patterns resembling anomalous energy performance of a building, and identifying patterns that indicate degradation of energy performance of a building over time. 